![]() Tianyuan Jin †, Yu Yang, Renchi Yang, Jieming Shi, Keke Huang, and Xiaokui Xiao. In Proceedings of the 2021 ACM SIGKDD Conference on Knowledge Discovery and Data Mining ( KDD'21): 3735–3744, Singapore, August 14-18, 2021. " Representation Learning for Predicting Customer Orders". Tongwen Wu †, Yu Yang, Yanzhi Li, Huiqiang Mao, Liming Li, Xiaoqing Wang, and Yuming Deng. In Proceedings of the 39th International Conference on Machine Learning ( ICML 2022), PMLR 162:26116-26134. " Stochastic Continuous Submodular Maximization: Boosting via Non-oblivious Function". Qixin Zhang, Zengde Deng, Zaiyi Chen, Haoyuan Hu and Yu Yang.In Proceedings of the Thirty-Sixth Conference on Neural Information Processing Systems ( NeurIPS 2022). In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics ( AISTATS 2023). " Online Learning for Non-monotone DR-Submodular Maximization: From FullInformation to Bandit Feedback". Qixin Zhang, Zengde Deng, Zaiyi Chen, Kuangqi Zhou, Haoyuan Hu and Yu Yang.Accepted for the IEEE/ACM International Symposium on Quality of Service 2023 ( IWQoS 2023). "Ambient Backscatter with a Single Commodity AP". In Proceedings of the 40th International Conference on Machine Learning ( ICML 2023), PMLR 202:41786-41818. " Nearly Optimal Competitive Ratio for Online Allocation Problems with Two-sided Resource Constraints and Finite Requests". Qixin Zhang *, Wenbing Ye *, Zaiyi Chen *, Haoyuan Hu, Enhong Chen and Yu Yang.Knowledge and Information Systems, Volume 53, Issue 1, pages 43–70, October 2017, Springer-Verlag. " Measuring In-Network Node Similarity Based on Neighborhoods: A Unified Parametric Approach". Yu Yang, Jian Pei and Abdullah Al-Barakati.ACM Transactions on Knowledge Discovery from Data, Volume 11, Issue 3, Article No. Qi Liu, Biao Xiang, Nicholas Jing Yuan, Enhong Chen, Hui Xiong, Yi Zheng, and Yu Yang, " An Influence Propagation View of PageRank". IEEE Transactions on Knowledge and Data Engineering, Volume 29, Issue 11, pages 2615-2628, November 2017, IEEE Computer Society. " Tracking Influential Individuals in Dynamic Networks". Yu Yang, Zhefeng Wang, Jian Pei and Enhong Chen.IEEE Transactions on Knowledge and Data Engineering, Volume 29, Issue 11, pages 2374-2387, November 2017, IEEE Computer Society. " Activity Maximization by Effective Information Diffusion in Social Networks". Zhefeng Wang *, Yu Yang *, Jian Pei, Lingyang Chu and Enhong Chen. " Influence efficiency maximization: How can we spread information efficiently?". Xiang Zhu, Zhefeng Wang, Yu Yang, Bin Zhou and Yan Jia. ACM Transactions on Knowledge Discovery from Data, Volume 14, No. Yu Yang, Xiangbo Mao, Jian Pei and Xiaofei He.IEEE Transactions on Knowledge and Data Engineering, Volume 33, Issue 3, pages 1045-1063, March 2021, IEEE Computer Society. " Influence Analysis in Evolving Networks: A Survey". Computers & Operations Research, Volume 140, April 2022, 105675. " Just-in-time single-batch-processing machine scheduling". Journal of Manufacturing Systems, Volume 63, April 2022, Pages 593-608. " Tackling temporal-dynamic service composition in cloud manufacturing systems: A tensor factorization-based two-stage approach". Yang Hu, Feng Wu, Yu Yang and Yongkui Liu.To appear in IEEE Transactions on Computational Social Systems. "An Empirical Study of User Engagement in Influencer Marketing on Weibo and WeChat" ( Early Access). Jun Wang, Yu Yang, Qi Liu, Zheng Fang, Shujuan Sun and Yabo Xu. ![]() To appear in International Journal of Production Research. ![]() " Dynamic Cloud Manufacturing Service Composition with Re-entrant Services: An Online Policy Perspective". To appear in IEEE Transactions on Knowledge and Data Engineering. ![]() "Dynamic Assortment Selection under Inventory and Limited Switches Constraints". Hongbin Zhang, Qixin Zhang, Feng Wu and Yu Yang.If you are passionate about research and eager to work on cutting-edge projects in data selection for efficient and robust training of large models, consider joining our research lab and working with me! You will have the opportunity to lead your own research projects or contribute to ongoing studies.Selected Publications Papers in Refereed Journals Starting from the Fall 2023, I am actively seeking motivated undergrad/graduate students at UCLA for collaboration. īefore coming to US, I was born and raised in Beijing, China. During that time, I collaborated closely with Quanshi Zhang and Jungseock Joo at on interpretable and fair computer vision. degree in Mathematics of Computation and Statistics, also from UCLA. Prior to pursuing my Ph.D., I earned my B.Sc. My research primarily focuses on understanding and improving large-scale training data for efficient and robust learning. candidate in Computer Science at University of California, Los Angeles (UCLA), where I am fortunate to be advised by Baharan Mirzasoleiman.
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